Hilbert-Huang Transform for Non-Linear Characterization of Speech Rhythm

A method for non-linear and non-stationary characterisation of speech rhythm is presented using Hilbert Huang Transform (HHT) of ‘Speech Unit Intervals’ (SUI) signals. SUI signals are supported by intervals duration between given speech units such as vowel, consonant, or syllable. While HHT is based on the combination of the Empirical Mode Decomposition (EMD) and the Hilbert transform of the provided Intrinsic Mode Functions (IMFs). Since EMD is a data-driven approach which includes both signal-dependent and timevariant filtering, HHT analysis on the SUI signals makes it possible non-linear and non-stationary characterisation of the speech rhythm. Investigations on the HHT based rhythmic features are presented in this paper: emotional speech classification is individually performed on rhythmic features, and obtained classification probabilities are fused with those provided by a typical state-ofthe-art emotion recognition system based on acoustic and prosodic features sets.